Co-Designing Mobile Serious Games to Support Patients With Psoriatic Arthritis and Chronic Pain: Mixed Methods Study
Bibliographic record
Abstract
Background: Serious games offer promising avenues for clinical care by enhancing patient engagement and delivering therapeutic benefits. In psoriatic arthritis (PsA), chronic pain contributes to emotional distress, functional limitations, and reduced well-being. While symptom-tracking apps exist, few digital interventions directly address chronic pain through engaging, therapeutic experiences tailored to patients' cognitive and physical needs. Objective: This study aimed to co-design mobile serious games-NoPain Games-to support patients with PsA in managing chronic pain. Conducted within the iPROLEPSIS Horizon Europe project, the study involved a multidisciplinary cocreation session with rheumatologists, researchers, and technical experts, followed by a usability feedback session with patients with PsA. The goal was to identify therapeutic priorities, refine game mechanics, and assess usability to inform the development of personalized, accessible digital interventions. Methods: A sequential mixed methods design was used. First, a 90-minute remote cocreation session was held with 14 experts (6 rheumatologists, 4 technical experts, and 4 researchers) from 3 European countries. Participants reviewed game storyboards and discussed therapeutic and design priorities. Thematic analysis of transcribed discussions identified key insights. Next, a 60-minute remote usability feedback session was conducted with 5 patients with PsA (aged 25-64 y), who interacted with 2 high-fidelity game prototypes (Space Oddity and Four Seasons). Usability was assessed using the System Usability Scale (SUS), and qualitative feedback was collected through moderated discussion. Item-level analysis using item characteristic curves provided deeper insight into usability perceptions and item sensitivity. All ethics requirements were met for both study phases. Results: Thematic analysis of the transcribed dialogs of the cocreation session revealed three core themes: (1) therapeutic benefits (pain distraction, memory enhancement, cognitive stimulation, stress reduction, and creative engagement); (2) game difficulty (balancing duration and complexity to sustain engagement without fatigue); and (3) accessibility and interaction (addressing physical limitations, optimizing touchscreen usability, and ensuring inclusive design). These insights informed the development of 2 NoPain Game prototypes, which received a SUS score of 79 (SD 10.4; 95% CI 69.89-88.11), indicating good usability. Item characteristic curve analysis showed strong discrimination for learnability, while ease of use and confidence exhibited ceiling effects. Items like support needs and inconsistency showed minimal variability, and the learning curve demonstrated delayed but meaningful responsiveness at higher usability levels. Qualitative feedback reinforced the relevance of difficulty adjustment, technical refinements, and game mechanics, offering actionable insights for future iterations and broader implementation. Conclusions: This study uniquely contributes to the field by co-designing mobile serious games specifically for patients with PsA, integrating expert and patient input to address chronic pain through accessible, cognitively engaging digital interventions-an approach not previously explored in this population. Future work will refine game mechanics, integrate adaptive difficulty, and conduct clinical trials to evaluate therapeutic efficacy. NoPain Games may serve as complementary tools within digital care ecosystems, offering support tailored to the therapeutic needs and physical limitations of patients with PsA underserved by conventional therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".